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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from typing import Tuple, Optional
class ViVQAX_Model(nn.Module):
"""
ViVQAX_Model: A model for Visual Question Answering with explanation generation.
"""
def __init__(self,
vocab_size: int,
embed_size: int,
hidden_size: int,
num_layers: int,
num_answers: int,
max_explanation_length: int,
word2idx: dict,
dropout: float = 0.5):
super().__init__()
self.word2idx = word2idx
self.max_explanation_length = max_explanation_length
self.vocab_size = vocab_size
# Image Encoder (ResNet-50)
resnet = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
modules = list(resnet.children())[:-1]
self.resnet = nn.Sequential(*modules)
for p in self.resnet.parameters():
p.requires_grad = False
# Project image features to hidden size
self.image_projection = nn.Sequential(
nn.Linear(resnet.fc.in_features, hidden_size),
nn.ReLU(),
nn.Dropout(dropout)
)
# Question Encoder
self.embedding = nn.Embedding(vocab_size, embed_size)
self.question_lstm = nn.LSTM(
embed_size,
hidden_size,
num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0,
bidirectional=True
)
# Multimodal Fusion
fusion_size = hidden_size * 3 # image + bidirectional question
self.fusion = nn.Sequential(
nn.Linear(fusion_size, hidden_size),
nn.ReLU(),
nn.Dropout(dropout)
)
# Answer Prediction
self.answer_classifier = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size // 2, num_answers)
)
# Explanation Generator
self.explanation_lstm = nn.LSTM(
embed_size + num_answers + hidden_size, # word embedding + answer distribution + context
hidden_size,
num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0
)
self.explanation_output = nn.Linear(hidden_size, vocab_size)
def encode_image(self, image: torch.Tensor) -> torch.Tensor:
"""Extract and project image features."""
with torch.no_grad():
features = self.resnet(image)
features = features.squeeze(-1).squeeze(-1)
return self.image_projection(features)
def encode_question(self, question: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""Encode question sequence."""
embedded = self.embedding(question)
outputs, (hidden, cell) = self.question_lstm(embedded)
# Combine bidirectional states
hidden = torch.cat([hidden[-2], hidden[-1]], dim=1)
return outputs, hidden
def forward(self,
image: torch.Tensor,
question: torch.Tensor,
target_explanation: Optional[torch.Tensor] = None,
teacher_forcing_ratio: float = 0.5) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Forward pass of the model.
Args:
image: Input image tensor [batch_size, channels, height, width]
question: Question token indices [batch_size, question_length]
target_explanation: Optional target explanation for training [batch_size, max_length]
teacher_forcing_ratio: Probability of using teacher forcing during training
Returns:
Tuple of answer logits and explanation token logits
"""
batch_size = image.size(0)
device = image.device
# Encode inputs
img_features = self.encode_image(image)
question_outputs, question_hidden = self.encode_question(question)
# Fuse multimodal features
fused = self.fusion(torch.cat([img_features, question_hidden], dim=1))
# Predict answer
answer_logits = self.answer_classifier(fused)
answer_probs = F.softmax(answer_logits, dim=1)
# Initialize explanation generation
explanation_hidden = None
decoder_input = torch.tensor([[self.word2idx['<START>']]] * batch_size, device=device)
decoder_context = fused.unsqueeze(1).repeat(1, 1, 1)
explanation_outputs = []
max_length = self.max_explanation_length if target_explanation is None else target_explanation.size(1)
# Generate explanation tokens
for t in range(max_length - 1):
decoder_embedding = self.embedding(decoder_input)
decoder_input_combined = torch.cat([
decoder_embedding,
answer_probs.unsqueeze(1),
decoder_context
], dim=2)
output, explanation_hidden = self.explanation_lstm(
decoder_input_combined,
explanation_hidden
)
output = self.explanation_output(output)
explanation_outputs.append(output)
# Teacher forcing
if target_explanation is not None and torch.rand(1) < teacher_forcing_ratio:
decoder_input = target_explanation[:, t:t+1]
else:
decoder_input = output.argmax(2)
explanation_outputs = torch.cat(explanation_outputs, dim=1)
return answer_logits, explanation_outputs
def _length_penalty(self, length, alpha=0.8):
# Google NMT style length penalty
return ((5 + length) ** alpha) / ((5 + 1) ** alpha)
def _violates_no_repeat_ngram(self, seq, next_tok, n=2):
if len(seq) < n - 1:
return False
ngram = tuple(seq[-(n-1):] + [next_tok])
for i in range(len(seq) - n + 1):
if tuple(seq[i:i+n]) == ngram:
return True
return False
def _apply_repetition_penalty(self, logits, seq, penalty=1.5):
# Reduce logits of tokens that already appeared in the sequence
if len(seq) <= 1:
return logits
uniq_tokens = list(set(seq[1:])) # skip <START>
penalty_value = torch.log(torch.tensor(penalty, device=logits.device))
# Support both shape [V] and [B, V]
if logits.dim() == 1:
logits[uniq_tokens] -= penalty_value
else:
logits[:, uniq_tokens] -= penalty_value
return logits
def generate_explanation(self,
image: torch.Tensor,
question: torch.Tensor,
max_length: Optional[int] = None,
beam_size: int = 3) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Generate explanation using beam search.
Args:
image: Input image tensor
question: Question token indices
max_length: Maximum explanation length (optional)
beam_size: Beam size for beam search
Returns:
Tuple of answer logits and generated explanation indices
"""
batch_size = image.size(0)
device = image.device
max_length = max_length or self.max_explanation_length
# Encode and get answer
img_features = self.encode_image(image)
question_outputs, question_hidden = self.encode_question(question)
fused = self.fusion(torch.cat([img_features, question_hidden], dim=1))
answer_logits = self.answer_classifier(fused)
answer_probs = F.softmax(answer_logits, dim=1)
# Initialize beams for each batch item
beams = [[(0.0, [self.word2idx['<START>']], None, None)] for _ in range(batch_size)]
# Beam search
for _ in range(max_length - 1):
new_beams = [[] for _ in range(batch_size)]
for i in range(batch_size):
candidates = []
for score, seq, hidden_h, hidden_c in beams[i]:
if seq[-1] == self.word2idx['<END>']:
new_beams[i].append((score, seq, hidden_h, hidden_c))
continue
# Prepare decoder input
decoder_input = torch.tensor([seq[-1]], device=device)
decoder_embedding = self.embedding(decoder_input)
decoder_context = fused[i:i+1].unsqueeze(1)
decoder_input_combined = torch.cat([
decoder_embedding.unsqueeze(0),
answer_probs[i:i+1].unsqueeze(1),
decoder_context
], dim=2)
# Get next token probabilities
if hidden_h is None:
output, (hidden_h, hidden_c) = self.explanation_lstm(decoder_input_combined)
else:
output, (hidden_h, hidden_c) = self.explanation_lstm(
decoder_input_combined,
(hidden_h, hidden_c)
)
# Shape to [V] for simpler top-k handling
logits = self.explanation_output(output).squeeze(0).squeeze(0) # [V]
logits = self._apply_repetition_penalty(logits, seq, penalty=1.5)
probs = F.log_softmax(logits, dim=-1) # [V]
# Add top-k candidates
topk_probs, topk_indices = probs.topk(beam_size) # [K]
for prob, idx in zip(topk_probs, topk_indices):
idx_item = idx.item()
# no-repeat n-gram
if self._violates_no_repeat_ngram(seq, idx_item, n=2):
continue
new_score = score + prob.item()
candidates.append((
new_score,
seq + [idx_item],
hidden_h,
hidden_c
))
# Select top beam_size candidates
candidates.sort(key=lambda x: x[0], reverse=True)
new_beams[i] = candidates[:beam_size]
# Early stopping
all_done = True
end_id = self.word2idx['<END>']
for i in range(batch_size):
if not new_beams[i]:
continue
done_i = all((seq[-1] == end_id) for _, seq, _, _ in new_beams[i])
if not done_i:
all_done = False
break
if all_done:
beams = new_beams
break
beams = new_beams
# Select best sequence from each beam
generated_explanations = []
for beam in beams:
if not beam:
generated_explanations.append(torch.tensor([self.word2idx['<PAD>']], device=device))
else:
best_seq = max(beam, key=lambda x: x[0] / self._length_penalty(len(x[1])))[1]
generated_explanations.append(torch.tensor(best_seq, device=device))
# Pad sequences
generated_explanations = torch.nn.utils.rnn.pad_sequence(
generated_explanations,
batch_first=True,
padding_value=self.word2idx['<PAD>']
)
return answer_logits, generated_explanations |